from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

print_time_report()

Time report

sklearnex_KMeans_short: 0h 0m 1s

sklearnex_Ridge: 0h 0m 2s

KMeans_short: 0h 0m 3s

sklearnex_LogisticRegression: 0h 0m 4s

sklearnex_KMeans_tall: 0h 0m 9s

Ridge: 0h 0m 12s

LogisticRegression: 0h 0m 22s

KMeans_tall: 0h 0m 25s

sklearnex_KNeighborsClassifier_kd_tree: 0h 0m 30s

KNeighborsClassifier_kd_tree: 0h 2m 54s

sklearnex_KNeighborsClassifier: 0h 3m 10s

catboost_lossguide: 0h 5m 2s

xgboost: 0h 5m 10s

lightgbm: 0h 5m 12s

HistGradientBoostingClassifier: 0h 5m 16s

catboost_symmetric: 0h 5m 33s

KNeighborsClassifier: 0h 34m 32s

total: 1h 8m 44s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.8.0-1036-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.3",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.21.0",
    "scipy": "1.7.0",
    "Cython": null,
    "pandas": "1.3.0",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.191	0.0	4.187	0.0	1	5	NaN	NaN	0.508	0.0	0.376	0.0	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.143	0.0	5.595	0.0	1	1	NaN	NaN	0.496	0.0	0.288	0.0	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.138	0.0	5.796	0.0	1	100	NaN	NaN	0.497	0.0	0.278	0.0	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.131	0.0	6.107	0.0	-1	1	NaN	NaN	0.499	0.0	0.263	0.0	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.134	0.0	5.984	0.0	-1	5	NaN	NaN	0.499	0.0	0.268	0.0	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.130	0.0	6.155	0.0	-1	100	NaN	NaN	0.501	0.0	0.260	0.0	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.062	0.0	0.257	0.0	1	5	NaN	NaN	0.116	0.0	0.537	0.0	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.063	0.0	0.253	0.0	1	1	NaN	NaN	0.115	0.0	0.550	0.0	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.061	0.0	0.262	0.0	1	100	NaN	NaN	0.115	0.0	0.530	0.0	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.063	0.0	0.255	0.0	-1	1	NaN	NaN	0.116	0.0	0.542	0.0	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.066	0.0	0.242	0.0	-1	5	NaN	NaN	0.117	0.0	0.564	0.0	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.066	0.0	0.243	0.0	-1	100	NaN	NaN	0.116	0.0	0.568	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	22.900	0.226	0.0	0.023	1	5	0.795	0.950	2.280	0.012	10.045	0.112	See	See
2	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.214	0.005	0.0	0.214	1	5	1.000	1.000	0.091	0.001	2.337	0.058	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	13.643	0.065	0.0	0.014	1	1	0.692	0.823	2.227	0.019	6.125	0.061	See	See
5	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.211	0.002	0.0	0.211	1	1	1.000	1.000	0.097	0.015	2.169	0.341	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	22.786	0.079	0.0	0.023	1	100	0.933	0.950	2.375	0.041	9.596	0.170	See	See
8	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.213	0.002	0.0	0.213	1	100	1.000	1.000	0.092	0.001	2.305	0.032	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	25.277	0.530	0.0	0.025	-1	1	0.692	0.823	2.291	0.021	11.032	0.253	See	See
11	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.194	0.022	0.0	0.194	-1	1	1.000	1.000	0.092	0.000	2.110	0.234	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	34.056	0.000	0.0	0.034	-1	5	0.795	0.718	2.290	0.013	14.874	0.082	See	See
14	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.200	0.021	0.0	0.200	-1	5	1.000	1.000	0.094	0.003	2.128	0.236	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	33.889	0.000	0.0	0.034	-1	100	0.933	0.718	2.288	0.027	14.812	0.174	See	See
17	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.197	0.024	0.0	0.197	-1	100	1.000	1.000	0.093	0.001	2.121	0.254	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	21.260	0.043	0.0	0.021	1	5	0.989	0.982	0.426	0.003	49.949	0.400	See	See
20	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.024	0.000	0.0	0.024	1	5	1.000	1.000	0.007	0.001	3.468	0.458	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	10.372	0.034	0.0	0.010	1	1	0.976	0.981	0.360	0.003	28.797	0.266	See	See
23	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.016	0.000	0.0	0.016	1	1	1.000	1.000	0.007	0.000	2.414	0.128	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	21.370	0.047	0.0	0.021	1	100	0.994	0.982	0.427	0.002	50.067	0.280	See	See
26	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.025	0.000	0.0	0.025	1	100	1.000	1.000	0.007	0.001	3.334	0.560	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	22.605	0.266	0.0	0.023	-1	1	0.976	0.981	0.366	0.001	61.782	0.755	See	See
29	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.025	0.004	0.0	0.025	-1	1	1.000	1.000	0.007	0.000	3.498	0.590	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	32.789	0.000	0.0	0.033	-1	5	0.989	0.974	0.363	0.005	90.239	1.189	See	See
32	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.032	0.002	0.0	0.032	-1	5	1.000	1.000	0.007	0.001	4.511	0.614	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	32.825	0.000	0.0	0.033	-1	100	0.994	0.974	0.361	0.003	90.913	0.852	See	See
35	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.032	0.002	0.0	0.032	-1	100	1.000	1.000	0.007	0.001	4.609	0.913	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.535	0.0	0.023	0.0	-1	5	NaN	NaN	0.803	0.0	4.400	0.0	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.691	0.0	0.022	0.0	-1	1	NaN	NaN	0.799	0.0	4.617	0.0	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.663	0.0	0.022	0.0	1	100	NaN	NaN	0.795	0.0	4.610	0.0	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.558	0.0	0.022	0.0	1	5	NaN	NaN	0.803	0.0	4.433	0.0	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.362	0.0	0.024	0.0	1	1	NaN	NaN	0.804	0.0	4.182	0.0	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.381	0.0	0.024	0.0	-1	100	NaN	NaN	0.798	0.0	4.235	0.0	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.893	0.0	0.018	0.0	-1	5	NaN	NaN	0.554	0.0	1.610	0.0	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.873	0.0	0.018	0.0	-1	1	NaN	NaN	0.541	0.0	1.613	0.0	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.867	0.0	0.018	0.0	1	100	NaN	NaN	0.543	0.0	1.597	0.0	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.895	0.0	0.018	0.0	1	5	NaN	NaN	0.547	0.0	1.637	0.0	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.870	0.0	0.018	0.0	1	1	NaN	NaN	0.555	0.0	1.568	0.0	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.868	0.0	0.018	0.0	-1	100	NaN	NaN	0.547	0.0	1.588	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.906	0.008	0.000	0.001	-1	5	0.973	0.967	0.610	0.010	1.486	0.028	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.005	0.001	0.000	0.005	-1	5	1.000	1.000	0.001	0.000	5.608	1.921	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.490	0.009	0.000	0.000	-1	1	0.964	0.946	0.109	0.001	4.477	0.094	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.005	0.001	0.000	0.005	-1	1	1.000	1.000	0.000	0.000	22.860	12.642	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	5.197	0.024	0.000	0.005	1	100	0.974	0.968	0.200	0.003	25.991	0.368	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.010	0.000	0.000	0.010	1	100	1.000	1.000	0.000	0.000	28.570	10.944	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	1.507	0.022	0.000	0.002	1	5	0.973	0.968	0.197	0.002	7.650	0.127	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.001	0.000	0.003	1	5	1.000	1.000	0.000	0.000	9.143	3.714	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.766	0.009	0.000	0.001	1	1	0.964	0.946	0.110	0.001	6.979	0.117	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.002	0.000	0.000	0.002	1	1	1.000	1.000	0.000	0.000	9.216	4.858	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	3.000	0.016	0.000	0.003	-1	100	0.974	0.967	0.601	0.009	4.993	0.078	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.013	0.002	0.000	0.013	-1	100	1.000	1.000	0.001	0.000	14.861	5.161	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.029	0.001	0.001	0.000	-1	5	0.975	0.982	0.007	0.001	3.836	0.499	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	19.021	12.678	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.026	0.001	0.001	0.000	-1	1	0.969	0.974	0.001	0.000	32.319	9.080	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	20.546	13.332	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.057	0.001	0.000	0.000	1	100	0.979	0.978	0.001	0.001	43.995	19.014	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.762	4.380	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.027	0.000	0.001	0.000	1	5	0.975	0.978	0.001	0.000	22.636	4.434	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.596	4.422	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.024	0.000	0.001	0.000	1	1	0.969	0.974	0.001	0.000	22.161	5.451	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.512	4.120	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.049	0.000	0.000	0.000	-1	100	0.979	0.982	0.008	0.001	6.411	1.069	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.000	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	19.938	14.183	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.665	0.0	0.722	0.0	random	NaN	30	NaN	0.528	0.0	1.259	0.0	See	See
3	KMeans_tall	fit	1000000	1000000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.657	0.0	0.730	0.0	k-means++	NaN	30	NaN	0.563	0.0	1.166	0.0	See	See
6	KMeans_tall	fit	1000000	1000000	100	42	20.0	200	sklearn.datasets.make_blobs	30	7.322	0.0	3.278	0.0	random	NaN	30	NaN	3.033	0.0	2.414	0.0	See	See
9	KMeans_tall	fit	1000000	1000000	100	42	20.0	200	sklearn.datasets.make_blobs	30	7.235	0.0	3.317	0.0	k-means++	NaN	30	NaN	3.222	0.0	2.246	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.301	0.000	random	0.001	30	0.000	0.0	0.0	8.780	4.502	See	See
2	KMeans_tall	predict	1000000	1	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.000	0.002	random	1.000	30	1.000	0.0	0.0	11.926	7.712	See	See
4	KMeans_tall	predict	1000000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.306	0.000	k-means++	0.001	30	0.000	0.0	0.0	8.735	4.766	See	See
5	KMeans_tall	predict	1000000	1	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	11.149	6.661	See	See
7	KMeans_tall	predict	1000000	1000	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	11.227	0.000	random	0.002	30	0.001	0.0	0.0	6.691	2.705	See	See
8	KMeans_tall	predict	1000000	1	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.016	0.002	random	1.000	30	1.000	0.0	0.0	9.453	4.795	See	See
10	KMeans_tall	predict	1000000	1000	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	11.975	0.000	k-means++	0.001	30	0.002	0.0	0.0	5.846	2.497	See	See
11	KMeans_tall	predict	1000000	1	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.016	0.002	k-means++	1.000	30	1.000	0.0	0.0	10.155	5.696	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.106	0.0	0.030	0.0	random	NaN	20	NaN	0.039	0.0	2.712	0.0	See	See
3	KMeans_short	fit	10000	10000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.292	0.0	0.011	0.0	k-means++	NaN	20	NaN	0.101	0.0	2.878	0.0	See	See
6	KMeans_short	fit	10000	10000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.261	0.0	0.614	0.0	random	NaN	20	NaN	0.148	0.0	1.760	0.0	See	See
9	KMeans_short	fit	10000	10000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.726	0.0	0.220	0.0	k-means++	NaN	20	NaN	0.401	0.0	1.812	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.001	0.131	0.000	random	0.001	20	-0.001	0.001	0.0	3.954	1.227	See	See
2	KMeans_short	predict	10000	1	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.000	0.000	0.002	random	1.000	20	1.000	0.000	0.0	10.794	6.408	See	See
4	KMeans_short	predict	10000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.001	0.129	0.000	k-means++	0.000	20	-0.002	0.001	0.0	3.730	1.043	See	See
5	KMeans_short	predict	10000	1	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.000	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	10.908	6.615	See	See
7	KMeans_short	predict	10000	1000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.003	0.000	5.193	0.000	random	0.366	20	0.265	0.001	0.0	2.444	0.388	See	See
8	KMeans_short	predict	10000	1	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.002	0.000	0.010	0.002	random	1.000	20	1.000	0.000	0.0	8.020	3.699	See	See
10	KMeans_short	predict	10000	1000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.003	0.000	5.483	0.000	k-means++	0.260	20	0.299	0.001	0.0	2.385	0.337	See	See
11	KMeans_short	predict	10000	1	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.002	0.000	0.010	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.208	3.749	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_informative	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	5	5	0	42	sklearn.datasets.make_classification	[20]	12.729	0.0	[-0.09268799]	0.000	NaN	NaN	NaN	NaN	NaN	2.166	0.0	5.877	0.0	See	See
3	LogisticRegression	fit	1000	1000	10000	5	5	0	42	sklearn.datasets.make_classification	[27]	0.920	0.0	[-2.31968832]	0.001	NaN	NaN	NaN	NaN	NaN	0.836	0.0	1.101	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_informative	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	5	5	0	42	sklearn.datasets.make_classification	[20]	0.000	0.0	[45.48626955]	0.0	NaN	NaN	NaN	NaN	0.571	0.000	0.0	0.823	0.310	See	See
2	LogisticRegression	predict	1000000	1	100	5	5	0	42	sklearn.datasets.make_classification	[20]	0.000	0.0	[0.15337835]	0.0	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.543	0.530	See	See
4	LogisticRegression	predict	1000	100	10000	5	5	0	42	sklearn.datasets.make_classification	[27]	0.002	0.0	[111.85254272]	0.0	NaN	NaN	NaN	NaN	0.320	0.003	0.0	0.579	0.078	See	See
5	LogisticRegression	predict	1000	1	10000	5	5	0	42	sklearn.datasets.make_classification	[27]	0.000	0.0	[19.58693334]	0.0	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.141	0.091	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_informative	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	10	42	sklearn.datasets.make_regression	NaN	0.205	0.0	0.391	0.0	NaN	NaN	NaN	0.210	0.0	0.974	0.0	See	See
3	Ridge	fit	1000000	1000000	100	10	42	sklearn.datasets.make_regression	NaN	1.610	0.0	0.497	0.0	NaN	NaN	NaN	0.286	0.0	5.629	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_informative	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	10	42	sklearn.datasets.make_regression	NaN	0.01	0.0	7.847	0.0	NaN	NaN	0.118	0.017	0.0	0.591	0.022	See	See
2	Ridge	predict	1000	1	10000	10	42	sklearn.datasets.make_regression	NaN	0.00	0.0	0.973	0.0	NaN	NaN	NaN	0.000	0.0	0.617	0.503	See	See
4	Ridge	predict	1000000	1000	100	10	42	sklearn.datasets.make_regression	NaN	0.00	0.0	4.686	0.0	NaN	NaN	1.000	0.000	0.0	0.662	0.410	See	See
5	Ridge	predict	1000000	1	100	10	42	sklearn.datasets.make_regression	NaN	0.00	0.0	0.012	0.0	NaN	NaN	NaN	0.000	0.0	0.624	0.622	See	See